{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Multiple Plots\n",
    "Making multiple plots at the same time is easy in ggplot. Plots can either be show inline using the `print` function, the `__repr__` (what python prints out by default for something), or ggplot's `save` function.\n",
    "\n",
    "Since we're trying to make multiple plots, the `__repr__` example doesn't really appy here, so we'll be focusing on using `print` and `save` to make multiple plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say you want to generate a histogram of price data for each type of diamond cut in the `diamonds` dataset. First thing you need to do is split your dataset up into groups. You can do this using the `groupby` function in `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     carat   cut color clarity  depth  table  price     x     y     z\n",
      "8     0.22  Fair     E     VS2   65.1   61.0    337  3.87  3.78  2.49\n",
      "91    0.86  Fair     E     SI2   55.1   69.0   2757  6.45  6.33  3.52\n",
      "97    0.96  Fair     F     SI2   66.3   62.0   2759  6.27  5.95  4.07\n",
      "123   0.70  Fair     F     VS2   64.5   57.0   2762  5.57  5.53  3.58\n",
      "124   0.70  Fair     F     VS2   65.3   55.0   2762  5.63  5.58  3.66\n",
      "    carat   cut color clarity  depth  table  price     x     y     z\n",
      "2    0.23  Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
      "4    0.31  Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
      "10   0.30  Good     J     SI1   64.0   55.0    339  4.25  4.28  2.73\n",
      "17   0.30  Good     J     SI1   63.4   54.0    351  4.23  4.29  2.70\n",
      "18   0.30  Good     J     SI1   63.8   56.0    351  4.23  4.26  2.71\n",
      "    carat    cut color clarity  depth  table  price     x     y     z\n",
      "0    0.23  Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
      "11   0.23  Ideal     J     VS1   62.8   56.0    340  3.93  3.90  2.46\n",
      "13   0.31  Ideal     J     SI2   62.2   54.0    344  4.35  4.37  2.71\n",
      "16   0.30  Ideal     I     SI2   62.0   54.0    348  4.31  4.34  2.68\n",
      "39   0.33  Ideal     I     SI2   61.8   55.0    403  4.49  4.51  2.78\n",
      "    carat      cut color clarity  depth  table  price     x     y     z\n",
      "1    0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
      "3    0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "12   0.22  Premium     F     SI1   60.4   61.0    342  3.88  3.84  2.33\n",
      "14   0.20  Premium     E     SI2   60.2   62.0    345  3.79  3.75  2.27\n",
      "15   0.32  Premium     E      I1   60.9   58.0    345  4.38  4.42  2.68\n",
      "    carat        cut color clarity  depth  table  price     x     y     z\n",
      "5    0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n",
      "6    0.24  Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47\n",
      "7    0.26  Very Good     H     SI1   61.9   55.0    337  4.07  4.11  2.53\n",
      "9    0.23  Very Good     H     VS1   59.4   61.0    338  4.00  4.05  2.39\n",
      "19   0.30  Very Good     J     SI1   62.7   59.0    351  4.21  4.27  2.66\n"
     ]
    }
   ],
   "source": [
    "for i, group in diamonds.groupby(\"cut\"):\n",
    "    print group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to create and render a plot for each one of these subgroups, just make a ggplot object add a `geom_histogram` layer, and then print the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ZzMy0kfvOd74z73znO5Mkzz33XL7xjW/kV37lV/K1r30t+/bty4YNG7J///4MDg4mSQYH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\nAFCc/we+WRhw+f75MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102587350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (270895005)>\n"
     ]
    },
    {
     "data": {
      "image/png": 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AUAzxCwBAMcQvAADFEL8AABRD/AIAUAzxCwBAMcQvAADFEL8AABRD/AIAUAzxCwBAMcQv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\nxv8D5wGkRgkkSVIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f612210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (285808485)>\n"
     ]
    },
    {
     "data": {
      "image/png": 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SDAAAAOO55jrg4MGDOnHihAoLC7Vz505J0sTEhFpbWzU6OqqSkhI1NzfL4/FIkjo7O9XV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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c131e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (285107893)>\n"
     ]
    },
    {
     "data": {
      "image/png": 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np2fameDzTpw4kYmJiXk5WFhsS2GW29ralsRxLLTJyUnP2yyZsbkxY80xZ+Wa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\nAKAY/x+ZduRDltkvHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe66990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (285840221)>\n"
     ]
    },
    {
     "data": {
      "image/png": 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zn+broq7kzJkz2bp1a1avXp3bb789yffDdHh4ON3d3anX6+nq6kryg53es2q1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\nFEP8AgBQDPELAEAx/j/Fhq0UJwPu4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147c31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (289914273)>\n"
     ]
    }
   ],
   "source": [
    "for name, group in diamonds.groupby(\"cut\"):\n",
    "    p = ggplot(group, aes(x='price')) + geom_histogram() + ggtitle(name)\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to save the plots to a file instead, just use `save` instead of print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zn+broq7kzJkz2bp1a1avXp3bb789yffDdHh4ON3d3anX6+nq6kryg53es2q1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\nFEP8AgBQDPELAEAx/j/Fhq0UJwPu4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114833710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, group in diamonds.groupby(\"cut\"):\n",
    "    p = ggplot(group, aes(x='price')) + geom_histogram() + ggtitle(name)\n",
    "    filename = \"price-distribution-\" + name + \".png\"\n",
    "    p.save(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
